text: /*
   * Copyright (c) 2018-2020 Arm Limited.
   *
   * SPDX-License-Identifier: MIT
   *
   * Permission is hereby granted, free of charge, to any person obtaining a copy
   * of this software and associated documentation files (the "Software"), to
   * deal in the Software without restriction, including without limitation the
   * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
   * sell copies of the Software, and to permit persons to whom the Software is
   * furnished to do so, subject to the following conditions:
   *
   * The above copyright notice and this permission notice shall be included in all
   * copies or substantial portions of the Software.
   *
   * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
   * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
   * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
   * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
   * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
   * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
   * SOFTWARE.
   */
  #include "arm_compute/runtime/CL/functions/CLRNNLayer.h"
  
  #include "arm_compute/core/Helpers.h"
  #include "arm_compute/core/Types.h"
  #include "arm_compute/core/Utils.h"
  #include "arm_compute/core/utils/misc/ShapeCalculator.h"
  #include "arm_compute/runtime/CL/CLScheduler.h"
  #include "src/core/CL/kernels/CLCopyKernel.h"
  #include "src/core/CL/kernels/CLDepthConvertLayerKernel.h"
  #include "src/core/CL/kernels/CLFillBorderKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyNativeKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpMatrixMultiplyReshapedOnlyRHSKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpOffsetContributionOutputStageKernel.h"
  #include "src/core/CL/kernels/CLGEMMLowpReductionKernel.h"
  #include "src/core/CL/kernels/CLGEMMMatrixMultiplyKernel.h"
  #include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedKernel.h"
  #include "src/core/CL/kernels/CLGEMMMatrixMultiplyReshapedOnlyRHSKernel.h"
  #include "src/core/CL/kernels/CLGEMMReshapeLHSMatrixKernel.h"
  #include "src/core/CL/kernels/CLGEMMReshapeRHSMatrixKernel.h"
  #include "support/MemorySupport.h"
  
  namespace arm_compute
  {
  using namespace arm_compute::misc::shape_calculator;
  
  CLRNNLayer::CLRNNLayer(std::shared_ptr<IMemoryManager> memory_manager)
      : _memory_group(std::move(memory_manager)), _gemm_state_f(), _add_kernel(), _activation(), _fully_connected_kernel(), _copy_kernel(support::cpp14::make_unique<CLCopyKernel>()), _fully_connected_out(),
        _gemm_output(), _add_output(), _is_prepared(false)
  {
  }
  
  CLRNNLayer::~CLRNNLayer() = default;
  
  Status CLRNNLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *recurrent_weights, const ITensorInfo *bias, const ITensorInfo *hidden_state,
                              const ITensorInfo *output, const ActivationLayerInfo &info)
  {
      ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output);
      ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F16, DataType::F32);
      ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, recurrent_weights, bias, hidden_state, output);
  
      const int idx_width  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
      const int idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
  
      ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != weights->dimension(idx_width));
      ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_height) != recurrent_weights->dimension(idx_width));
      ARM_COMPUTE_RETURN_ERROR_ON(recurrent_weights->dimension(idx_width) != recurrent_weights->dimension(1));
      ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != 1);
      ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(idx_width) != weights->dimension(idx_height));
      ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_width) != weights->dimension(idx_height));
      ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_height) != input->dimension(idx_height));
      ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), hidden_state->tensor_shape());
  
      auto shape_info = TensorInfo(compute_rnn_shape(recurrent_weights, hidden_state->dimension(idx_height)), 1, input->data_type());
  
      ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, weights, bias, &shape_info));
      ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(hidden_state, recurrent_weights, nullptr, &shape_info, 1.f, 0.f));
      ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAddition::validate(&shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE));
      ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&shape_info, &shape_info, info));
  
      return Status{};
  }
  
  void CLRNNLayer::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *recurrent_weights, const ICLTensor *bias, ICLTensor *hidden_state, ICLTensor *output,
                             ActivationLayerInfo &info)
  {
      configure(CLKernelLibrary::get().get_compile_context(), input, weights, recurrent_weights, bias, hidden_state, output, info);
  }
  
  void CLRNNLayer::configure(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *recurrent_weights, const ICLTensor *bias,
                             ICLTensor *hidden_state,
                             ICLTensor *output, ActivationLayerInfo &info)
  {
      ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output);
      ARM_COMPUTE_ERROR_THROW_ON(CLRNNLayer::validate(input->info(), weights->info(), recurrent_weights->info(), bias->info(), hidden_state->info(), output->info(), info));
  
      const int   idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
      TensorShape shape      = compute_rnn_shape(recurrent_weights->info(), hidden_state->info()->dimension(idx_height));
  
      _is_prepared = false;
  
      _fully_connected_out.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
      _gemm_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
  
      // Manage intermediate buffers and configure
      _memory_group.manage(&_fully_connected_out);
      _fully_connected_kernel.configure(compile_context, input, weights, bias, &_fully_connected_out);
  
      _memory_group.manage(&_gemm_output);
      _gemm_state_f.configure(compile_context, hidden_state, recurrent_weights, nullptr, &_gemm_output, 1.f, 0.f);
  
      _add_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
      _memory_group.manage(&_add_output);
  
      _add_kernel.configure(compile_context, &_fully_connected_out, &_gemm_output, &_add_output, ConvertPolicy::SATURATE);
  
      _fully_connected_out.allocator()->allocate();
      _gemm_output.allocator()->allocate();
  
      _activation.configure(compile_context, &_add_output, hidden_state, info);
      _add_output.allocator()->allocate();
  
      _copy_kernel->configure(compile_context, hidden_state, output);
  }
  
  void CLRNNLayer::run()
  {
      prepare();
  
      MemoryGroupResourceScope scope_mg(_memory_group);
  
      _fully_connected_kernel.run();
      _gemm_state_f.run();
      _add_kernel.run();
      _activation.run();
  
      // copy hidden out to output
      CLScheduler::get().enqueue(*_copy_kernel);
  }
  
  void CLRNNLayer::prepare()
  {
      if(!_is_prepared)
      {
          _fully_connected_kernel.prepare();
          _gemm_state_f.prepare();
  
          _is_prepared = true;
      }
  }
  } // namespace arm_compute
  